A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns

With the development of maritime technology and equipment, most ships are equipped with an automatic identification system (AIS) to store navigation information. Over time, the size of the data increases, rendering its storage and processing difficult. Hence, it is necessary to transform the AIS data into trajectories, and then simplify the AIS trajectories to remove unnecessary information that is not related to route shape. Moreover, topographic information must be considered because otherwise, the simplified trajectory can intersect obstacles. In this study, we propose an AIS trajectory simplification algorithm considering topographic information. The proposed algorithm simplifies the trajectories without the intersection of the trajectory and obstacle using the improved Douglas–Peucker algorithm. Polygon map random (PMR) quadtree was used to consider topographic information on the coast, and the intersection between topographic information and simplified trajectories was efficiently computed using the PMR quadtree. To verify the effectiveness of the proposed algorithm, experiments were conducted on real-world trajectories in the Korean sea. The proposed algorithm yielded simplified trajectories with no intersections of the trajectory and obstacle. In addition, the computational efficiency of the proposed algorithm with the PMR quadtree was superior to that without the PMR quadtree.

“…The compression rate denotes how many points on the original trajectory were removed and can be expressed as Equation (7).…”

Section: Evaluation Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AIS Trajectories Simplification Algorithm Considering Topographic Information

Lee¹,

Cho²

2022

“…AIS provides not only static information about the ships, such as length, width, and ship type, but also dynamic information including latitude, longitude, and acquisition time. These pieces of information can be used for predicting ship behavior [ 3 , 4 , 5 , 6 ] and trajectories [ 7 , 8 ], supporting maritime search and rescue systems [ 9 , 10 ], detecting fishing activities [ 11 , 12 ], as well as identifying anomalous behavior [ 13 , 14 , 15 , 16 ]. This greatly facilitates maritime operations and enhances maritime safety governance.…”

Section: Introductionmentioning

confidence: 99%

Robust Learning with Noisy Ship Trajectories by Adaptive Noise Rate Estimation

Yang

Wang

Chen

et al. 2023

Ship trajectory classification is of great significance for shipping analysis and marine security governance. However, in order to cover up their illegal fishing or espionage activities, some illicit ships will forge the ship type information in the Automatic Identification System (AIS), and this label noise will significantly impact the algorithm’s classification accuracy. Sample selection is a common and effective approach in the field of learning from noisy labels. However, most of the existing methods based on sample selection need to determine the noise rate of the data through prior means. To address these issues, we propose a noise rate adaptive learning mechanism that operates without prior conditions. This mechanism is integrated with the robust training paradigm JoCoR (joint training with co-regularization), giving rise to a noise rate adaptive learning robust training paradigm called A-JoCoR. Experimental results on real-world trajectories provided by the Danish Maritime Authority verified the effectiveness of A-JoCoR. It not only realizes the adaptive learning of the data noise rate during the training process, but also significantly improves the classification performance compared with the original method.

“…Along with similar premises, other authors used the AIS data to understand and forecast vessels' trajectories [16,17] or understand their environmental impact [18,19]. Some applied their efforts to detect anomalies among AIS transmission [20][21][22][23][24] and the sub-patterns of vessel mobility [25][26][27][28]. However, few works have focused on detecting fishing activities using AIS data [29,30].…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Methodology for Fishing Activity Detection Using the Geometry behind the Trajectory of Multiple Vessels

Ferreira

Spadon

Soares

et al. 2022

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data play a significant role in tracking vessel activity and mapping mobility patterns such as those found during fishing activities. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology, it is shown how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry, highlighting changes in the vessel’s moving pattern, which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. We propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall F-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the geometry observed in the trajectory.